import pdb
import torch
from torch import nn
import math
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable
import torch.nn.functional as F
from other_layers import *

BatchNorm = nn.BatchNorm2d


__all__ = ['drn_d_54_cat_noi', 'drn_d_54_cat_sp', 'drn_d_54_cat3']


webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'

model_urls = {
    'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',
    'drn-c-42': webroot + 'drn_c_42-9d336e8c.pth',
    'drn-c-58': webroot + 'drn_c_58-0a53a92c.pth',
    'drn-d-22': webroot + 'drn_d_22-4bd2f8ea.pth',
    'drn-d-38': webroot + 'drn_d_38-eebb45f0.pth',
    'drn-d-54': webroot + 'drn_d_54-0e0534ff.pth',
    'drn-d-105': webroot + 'drn_d_105-12b40979.pth'
}


def conv3x3(in_planes, out_planes, stride=1, padding=1, dilation=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=padding, bias=False, dilation=dilation)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 dilation=(1, 1), residual=True):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride,
                             padding=dilation[0], dilation=dilation[0])
        self.bn1 = BatchNorm(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes,
                             padding=dilation[1], dilation=dilation[1])
        self.bn2 = BatchNorm(planes)
        self.downsample = downsample
        self.stride = stride
        self.residual = residual

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        if self.residual:
            out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 dilation=(1, 1), residual=True):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=dilation[1], bias=False,
                               dilation=dilation[1])
        self.bn2 = BatchNorm(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class DRN(nn.Module):

    def __init__(self, block, layers, num_classes=1000,
                 channels=(16, 32, 64, 128, 256, 512, 512, 512),
                 out_map=False, out_middle=False, pool_size=28, arch='D'):
        super(DRN, self).__init__()
        self.inplanes = channels[0]
        self.out_map = out_map
        self.out_dim = channels[-1]
        self.out_middle = out_middle
        self.arch = arch
        self.ret_fea = False

        if arch == 'C':
            self.conv1 = nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
                                   padding=3, bias=False)
            self.bn1 = BatchNorm(channels[0])
            self.relu = nn.ReLU(inplace=True)

            self.layer1 = self._make_layer(
                BasicBlock, channels[0], layers[0], stride=1)
            self.layer2 = self._make_layer(
                BasicBlock, channels[1], layers[1], stride=2)
        elif arch == 'D':
            self.layer0 = nn.Sequential(
                nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
                          bias=False),
                BatchNorm(channels[0]),
                nn.ReLU(inplace=True)
            )

            self.layer1 = self._make_conv_layers(
                channels[0], layers[0], stride=1)
            self.layer2 = self._make_conv_layers(
                channels[1], layers[1], stride=2)

        self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
        self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2)
        self.layer5 = self._make_layer(block, channels[4], layers[4], dilation=2,
                                       new_level=False)
        self.layer6 = None if layers[5] == 0 else \
            self._make_layer(block, channels[5], layers[5], dilation=4,
                             new_level=False)

        if arch == 'C':
            self.layer7 = None if layers[6] == 0 else \
                self._make_layer(BasicBlock, channels[6], layers[6], dilation=2,
                                 new_level=False, residual=False)
            self.layer8 = None if layers[7] == 0 else \
                self._make_layer(BasicBlock, channels[7], layers[7], dilation=1,
                                 new_level=False, residual=False)
        elif arch == 'D':
            self.layer7 = None if layers[6] == 0 else \
                self._make_conv_layers(channels[6], layers[6], dilation=2)
            self.layer8 = None if layers[7] == 0 else \
                self._make_conv_layers(channels[7], layers[7], dilation=1)

        if num_classes > 0:
            self.avgpool = nn.AvgPool2d(pool_size)
            self.fc = nn.Conv2d(self.out_dim, num_classes, kernel_size=1,
                                stride=1, padding=0, bias=True)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, BatchNorm):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1,
                    new_level=True, residual=True):
        assert dilation == 1 or dilation % 2 == 0
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                BatchNorm(planes * block.expansion),
            )

        layers = list()
        layers.append(block(
            self.inplanes, planes, stride, downsample,
            dilation=(1, 1) if dilation == 1 else (
                dilation // 2 if new_level else dilation, dilation),
            residual=residual))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, residual=residual,
                                dilation=(dilation, dilation)))

        return nn.Sequential(*layers)

    def _make_conv_layers(self, channels, convs, stride=1, dilation=1):
        modules = []
        for i in range(convs):
            modules.extend([
                nn.Conv2d(self.inplanes, channels, kernel_size=3,
                          stride=stride if i == 0 else 1,
                          padding=dilation, bias=False, dilation=dilation),
                BatchNorm(channels),
                nn.ReLU(inplace=True)])
            self.inplanes = channels
        return nn.Sequential(*modules)

    def forward(self, x,ret_fea=False):
        if self.arch == 'C':
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
        elif self.arch == 'D':
            x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        fea = []
        if self.layer6 is not None:
            x = self.layer6(x)
            fea.append(x)

        if self.layer7 is not None:
            x = self.layer7(x)
            fea.append(x)

        if self.layer8 is not None:
            x = self.layer8(x)
            fea.append(x)

        if ret_fea:
            return x

        x = self.avgpool(x)
        x = self.fc(x)
        x = x.view(x.size(0), -1)

        if self.ret_fea:
            return fea
        else:
            return x


def drn_c_26(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-26']))
    return model


def drn_c_42(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-42']))
    return model


def drn_c_58(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='C', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-c-58']))
    return model


def drn_d_22(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-22']))
    return model


def drn_d_24(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 2, 2], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-24']))
    return model


def drn_d_38(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-38']))
    return model


def drn_d_40(pretrained=False, **kwargs):
    model = DRN(BasicBlock, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-40']))
    return model


def drn_d_54(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 1, 1], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-54']))
    return model


def drn_d_56(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 6, 3, 2, 2], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-56']))
    return model


def drn_d_105(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 1, 1], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-105']))
    return model


def drn_d_107(pretrained=False, **kwargs):
    model = DRN(Bottleneck, [1, 1, 3, 4, 23, 3, 2, 2], arch='D', **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['drn-d-107']))
    return model


def drn_d_54_cat(num_classes, pretrained=True):
    base_model = drn_d_54(pretrained=pretrained)
    base_model.fc = cat_conv_linear(512, num_classes)
    return base_model

class drn_d_54_cat3(nn.Module):
    def __init__(self,num_classes, pretrained=True):
        super(drn_d_54_cat3,self).__init__()
        base_model = drn_d_54(pretrained=pretrained)
        self.base_model =nn.Sequential(*list(base_model.children())[:-2])
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
        self.fc = cat_conv_linear(512,num_classes)
    def logits(self, features):
        x =self.avgpool(features)
        x = self.fc(x)
        x = x.view(x.size(0), -1)
        return x
    def forward(self, x):
        x = self.base_model(x)
        x = self.logits(x)
        return x

class drn_d_54_cat_sp(nn.Module):
    def __init__(self,num_classes, pretrained=True):
        super(drn_d_54_cat_sp,self).__init__()
        base_model = drn_d_54(pretrained=pretrained)
        self.base_model =nn.Sequential(*list(base_model.children())[:-2])
        self.fc = cat_conv_linear_sp(512,num_classes)
    def forward(self, x):
        x = self.base_model(x)
        x = self.fc(x)
        return x

class drn_d_54_cat_noi(nn.Module):
    def __init__(self, num_classes, confusion_matrixs, pretrained=True):
        super(drn_d_54_cat_noi, self).__init__()
        base_model = drn_d_54(pretrained=pretrained)
        self.base_model = nn.Sequential(*list(base_model.children())[:-2])
        self.fc = NoiseAdaptCat(512, num_classes, confusion_matrixs)

    def forward(self, x):
        x = self.base_model(x)
        x = self.fc(x)
        return x

class drn_d_54_cat_merge(nn.Module):
    def __init__(self, num_classes, pretrained=True):
        super(drn_d_54_cat_merge, self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_linear(in_features=1024,out_features_list=num_classes)
        self.avgpool = nn.AdaptiveAvgPool2d(1)

    def forward(self, x):
        _,l7, l8 = self.base_model(x)
        x = torch.cat([l7,l8],dim=1)
        x = self.avgpool(x).view(x.size(0),-1)
        x =self.fc(x)
        return x

class drn_d_54_merge_sp(nn.Module):
    def __init__(self,num_classes, pretrained=True):
        super(drn_d_54_merge_sp,self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_conv_linear_sp(1024,num_classes)
    def forward(self, x):
        _, l7, l8 = self.base_model(x)
        x = torch.cat([l7, l8], dim=1)
        x =self.fc(x)
        return x


class drn_d_54_sp_merge(nn.Module):
    def __init__(self,num_classes, pretrained=True):
        super(drn_d_54_sp_merge,self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_conv_linear_sp_merge(in_features=(512,512),
                                           out_features_list=num_classes)
    def forward(self, x):
        _, l7, l8 = self.base_model(x)
        x =self.fc(l7,l8)
        return x

class drn_d_54_cat_barpool1(nn.Module):
    def __init__(self, num_classes, pretrained=True, k_size=(7,42)):
        super(drn_d_54_cat_barpool1, self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_linear(in_features=512*9,out_features_list=num_classes)
        self.avgpool = nn.AvgPool2d(kernel_size=k_size, stride=4)

    def forward(self, x):
        _, _, x = self.base_model(x)
        x = self.avgpool(x)
        x = x.view(x.size(0),-1)
        x =self.fc(x)
        return x

class drn_d_54_cat_barpool2(nn.Module):
    def __init__(self, num_classes, pretrained=True, k_size=(42,7)):
        super(drn_d_54_cat_barpool2, self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_linear(in_features=512*9,out_features_list=num_classes)
        self.avgpool = nn.AvgPool2d(kernel_size=k_size, stride=4)

    def forward(self, x):
        _, _, x = self.base_model(x)
        x = self.avgpool(x)
        x = x.view(x.size(0),-1)
        x =self.fc(x)
        return x

class drn_d_54_cat_lm(nn.Module):
    def __init__(self, num_classes, pretrained=True):
        super(drn_d_54_cat_lm, self).__init__()
        self.base_model = drn_d_54(pretrained=pretrained)
        self.base_model.ret_fea = True
        self.fc = cat_linear(in_features=512+100,out_features_list=num_classes)
        self.emd = nn.Linear(276,100)
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)

    def forward(self, x, lm_fea):
        _, _, x = self.base_model(x)
        x = self.avgpool(x)
        x = x.view(x.size(0),-1)
        lm_fea = self.emd(lm_fea)
        x = torch.cat([x, lm_fea],dim=1)
        x =self.fc(x)
        return x


if __name__ == '__main__':
    import numpy as np

    model = drn_d_54_cat_barpool1(num_classes=[5,10])

    x = torch.FloatTensor(3,3,336,336).zero_()
    x = torch.autograd.Variable(x)
    y = model(x)

    print y